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Abstract

Introduction
Obesity is a public health problem that is due in part to low levels of physical
activity. Physical activity levels are influenced by the built environment. We
examined how changes in the built environment affected residents’ physical
activity levels in a low-income, primarily African American neighborhood in New
Orleans.

Methods
We built a 6-block walking path and installed a school playground in an
intervention neighborhood. We measured physical activity levels in this
neighborhood and in 2 matched comparison neighborhoods by self-report, using
door-to-door surveys, and by direct observations of neighborhood residents
outside before (2006) and after (2008) the interventions. We used Pearson χ2
tests of independence to assess bivariate associations and logistic regression
models to assess the effect of the interventions.

Results
Neighborhoods were comparable at baseline in demographic composition, choice of
physical activity locations, and percentage of residents who participated in
physical activity. Self-reported physical activity increased over time in most
neighborhoods. The proportion of residents observed who were active increased
significantly in the section of the intervention neighborhood with the path
compared with comparison neighborhoods. Among residents who were observed
engaging in physical activity, 41% were moderately to vigorously active in the
section of the intervention neighborhood with the path compared with 24% and 38%
in the comparison neighborhoods at the postintervention measurement (P <
.001).

Conclusion
Changes to the built environment may increase neighborhood physical activity in
low-income, African American neighborhoods.

Introduction

Obesity is a serious and widespread problem in the United States. Nearly 34%
of American adults are obese, and 68% are either obese or overweight (1). In
2007, Louisiana had the third highest rate of obesity in the nation; according
to self-reported data, 30.7%
of adults were obese (2). Youth and adults in New Orleans are less
likely to be physically active than those in the rest of the country (3). Among
adults in New Orleans, 38% met the recommended levels of physical activity (PA)
compared to 49% nationally. These findings were even more pronounced among low-
and moderate-income African Americans (2).

Features of the built environment influence the propensity to be physically
active (4-7). PA can be facilitated or constrained by the built environment,
although the relationship between individual factors, social factors, and the
physical environment is complex and not well understood (8). Making changes to
the built environment should be considered as a means of addressing the related
problems of obesity and physical inactivity (9). The Institute of Medicine has
identified improvements to the built environment that can encourage walking and
bicycling, such as a well-connected network of off-street trails and paths and
paths connecting destinations for such activity, as a priority (10).

The Prevention Research Center (PRC) at Tulane University works to identify
and address physical and social environmental factors that influence the obesity
epidemic and has an overall goal of reducing obesity and its associated health
problems. In this project, the Partnership for an Active Community Environment
(PACE), the PRC worked with neighborhood-based community groups to create
improvements to the built environment that would facilitate PA. Taking a
community-based participatory research approach (11), we recruited members of
local organizations to participate in the project steering committee. The
objective of this study was to asses the effect of improvements to the built
environment on the PA levels of residents in a low-income, African American
community.

Methods

We used a serial cross-sectional study design to evaluate the effect of the
installation of a path and playground on community-wide PA. We conducted
cross-sectional assessments at baseline (fall of 2006) and follow-up (fall of
2008). The changes to the built environment occurred in 2007. The study protocol
received approval from the Tulane University institutional review board.

Setting

In keeping with principles of community-based participatory research (11), we
chose the PACE project intervention neighborhood on the basis of long-standing
relationships between neighborhood leaders and Tulane University investigators
(12). Meeting monthly, members of local community organizations and Tulane
University faculty and staff formed the PACE steering committee
(Appendix).
The committee helped establish boundaries for the intervention neighborhood,
identified 2 comparison neighborhoods, and chose to install a walking path and
support the installation and use of a school playground as the built environment
interventions. Neighborhoods were matched as closely as possible on proportion
of homes owned by their residents, education level and annual household income of
residents, percentage of African American residents, and similarity of the built
environment, including housing and business type (13).

We also considered neighborhood flood levels at the time of Hurricane Katrina
(August 29, 2005) to ensure that a similar level of damage and rebuilding in
each of the neighborhoods existed (14). The chosen neighborhoods were reoccupied
and in the process of being rebuilt by the time baseline assessments were
conducted in the fall of 2006. All 3 neighborhoods were urban communities
composed of single-family homes, apartments in old houses, small apartment
complexes, and small businesses, and had sidewalks on nearly every street,
although many were in poor condition. The intervention neighborhood and 1
comparison neighborhood each had a single large playground, both of which were
taken over by the Federal Emergency Management Agency and used as a trailer
park, making them unavailable for public use during the study period. There was
no such space in the other comparison neighborhood. Streets and sidewalks were
the only available places for PA in the neighborhoods. The schools in each
neighborhood all had fenced, locked, concrete-slab yards with no playground
equipment. One comparison neighborhood was approximately 1.5 miles and the other
5.4 miles from the intervention neighborhood. Because railroad tracks
divided the intervention neighborhood, the steering committee recommended 2
interventions, 1 on each side of the tracks: the path in Area A and the
playground in Area B (Figure 1).

Figure 1. Partnership for an Active Community
Environment Study Areas, New Orleans, Louisiana.

Intervention

The PACE steering committee chose to install a walking path in Area A of the
intervention neighborhood (INA). In November 2007, PACE and the city of New
Orleans built an 8-foot-wide path of 6 blocks on a grassy, tree-filled median of
a wide neighborhood boulevard. The path connected a park outside the
intervention area to a commercial corridor.

In another intervention, in May 2007, KaBoom! (http://kaboom.org), a
national nonprofit organization, installed a playground with the help of
community members and organizations on the back lot of a local elementary school
in Area B of the intervention neighborhood (INB). The PACE project paid
supervisors to keep the fenced playground open after school hours and on
weekends from summer 2007 through spring 2009.

Household survey

We measured self-reported PA in the intervention neighborhood and the 2
comparison neighborhoods through interviewer-administered household surveys
conducted door to door (survey instruments available upon request to
corresponding author). The sampling plan consisted of 2 separate stratified
random samples of households before (September 2006 through February 2007) and
after (October 2008 through January 2009) the walking path and the playground
were built. A total of 6,497 households were in the 3 areas; 3,115 in the
intervention neighborhood, 943 in the first comparison neighborhood, and 2,439
in the second comparison neighborhood. Trained interviewers orally administered
the survey, which assessed the community social environment, the community
physical environment, and self-reported PA, health and well-being, height,
weight, and demographic characteristics. Interviewers randomly selected from
each household 1 English-speaking adult aged 18 to 70 who had lived in the
neighborhood for at least 3 months. Interviewers made up to 12 attempts to reach
that person. People were excluded if they did not speak English, had not lived
in the neighborhood for at least 3 months, or were outside the age range. PA
questions covered walking for leisure, walking for transportation, and engaging
in other activities such as bicycling or jogging. We also asked about use of
specific locations for such activity.

At baseline, we sampled 778 households and conducted 499 interviews (response
rate, 64.1%): 113 (out of 184) in INA, 111 (out of 174) in INB, 159 (out of 255)
in comparison neighborhood 1 (CN1), and 116 (out of 165) in comparison
neighborhood 2 (CN2). Of people sampled, we were unable to contact 112 (14.4%):
36 in INA, 21 in INB, 33 in CN1, and 22 in CN2. At follow-up, we sampled 900
households and conducted 692 interviews (response rate, 76.9%): 144 (out of 179)
in INA, 192 (out of 253) in INB, 169 (out of 204) in CN1, and 187 (out of 264)
in CN2. Of people sampled, we were unable to contact 109 (12.1%): 22 in INA, 26
in INB, 9 in CN1, and 52 in CN2.

Physical activity observations

We used adapted SOPLAY (System for Observing Play and Leisure Activity in
Youth) methods to objectively measure neighborhood PA on streets, sidewalks, and
outside public areas on every block in each of the 3 neighborhoods (15). The
basis of the SOPLAY system is momentary time sampling; it uses group time
sampling techniques (15) and counts the number of people and their PA levels
during play and leisure opportunities. Observers can use this technique in a
neighborhood setting, where spontaneous activity of varying levels occurs among
a changing number of people. Our interest was entire neighborhoods, so observers
drove through neighborhood streets to count people. We established driving
routes to cover all streets. Trained observers scanned the blocks, counting the
numbers of sedentary, walking (moderate PA), and very active (vigorous PA)
people, including youth and adult men and women. The
protocol defined “vigorous” as any activity that was more active than walking,
including running, lifting, bicycling, pushing, carrying, and dancing. We
recorded and controlled for contextual factors such as weather. Unusual events
such as street parties were noted. During observations, we divided the
intervention neighborhood into 2 sections (INA and INB).

Data collectors conducted PA observations in the afternoons between 4:00 pm
and 6:00 pm, 3 days per week (Thursday, Saturday, and Sunday), for 6 weeks (from
October through early December, excluding the week of Thanksgiving) in 2006 and
2008.

Analysis

We examined self-reported PA in several ways. Respondents indicated
dichotomously (yes/no) whether they walked for transportation and walked for
leisure (which included walking for exercise or walking a dog). We created 2
binary variables (1 for transportation and 1 for leisure) to indicate walking at
least 30 minutes per day for at least 5 days per week and created a single
binary variable to indicate walking for transportation, leisure, both, or not
walking. Results were similar for the 3 approaches and were, therefore, only
reported for the first. Frequencies of other forms of PA were low and were not
included.

We compared neighborhoods at baseline and follow-up for the proportion of
observed people that were moderately or vigorously active, for self-reported PA
and location of activity, and for sociodemographic characteristics. We treated
the 2 sections of the intervention neighborhood as separate neighborhoods, which
required 3 dummy variables to code the intervention neighborhood.

We computed Pearson χ2 statistics to explore the bivariate
relationships and used logistic regression to explore the effect of the
intervention. We considered age as a confounder and potential effect modifier.
Regression models included neighborhood, time, and neighborhood-by-time
interactions. If the neighborhood-by-time interaction was significant, we used
post hoc tests to determine whether the intervention neighborhood sections
changed more than the comparison neighborhoods. We set significance at P
< .05 and used SAS version 9.1 (SAS Institute, Inc, Cary, North Carolina) or Stata version 9.0 (StataCorp LP, College Station, Texas) for analyses. Everyone
who took the survey completed it, and less than 5% of responses were missing. No
imputation was conducted.

Results

Survey respondents from the 3 neighborhoods were similar demographically at
baseline (Table 1). Walking was the most common PA, and participation in any
other activity was low (data not shown). Self-reported walking for both
transportation and leisure increased from baseline to follow-up for most
neighborhoods (Table 2). Because this increase was similar for each
neighborhood, the neighborhood-by-time interactions were not significant. Age
was neither a confounder nor an effect modifier (data not shown).

People from all neighborhoods reported that they most frequently exercised at
sidewalks, mall or stores, and streets (Table 3). No significant
neighborhood-by-time interactions were found. Walking trail use increased
slightly but nonsignificantly (from 21.9% to 29.6%) in INA.

We found a significant neighborhood-by-time interaction between baseline and
follow-up for the proportion of people observed who were active. A significant
increase in the proportion of people engaged in moderate and vigorous activity
was noted in INA between baseline (36.7%) and follow-up (41.0%) (Pearson χ2
test, P < .001) (Figure 2).

Figure 2. Percentage of people observed in intervention
and comparison neighborhoods engaged in moderate and vigorous physical activity,
at baseline (2006) and follow-up (2008), Partnership for an Active Community
Environment Project, New Orleans, Louisiana. Neighborhood-by-time
interaction was significant (χ2 test, P = .001), and
differences between baseline and follow-up for INA and CN1 were significant
(Pearson χ2 test, P < .001). Abbreviations: INA, Area A of
intervention neighborhood (path); INB, Area B of intervention neighborhood
(playground); CN1, comparison neighborhood 1; CN2, comparison neighborhood 2. [A
tabular version of this figure is also
available.]

We observed a slight, significant increase in vigorous activity from 10.5% to
13.7% in that same area (Pearson χ2 test, P < .001) (Figure
3).

Figure 3. Percentage of people observed in intervention
and comparison neighborhoods engaged in vigorous physical activity, at baseline
(2006) and follow-up (2008), Partnership for an Active Community Environment Project, New Orleans, Louisiana. Neighborhood-by-time interaction was
significant (χ2 test, P = .001), and the difference between
baseline and follow-up for INA was significant (Pearson χ2 test,
P < .001). Abbreviations: INA, Area A of intervention neighborhood (path);
INB, Area B of intervention neighborhood (playground); CN1, comparison
neighborhood 1; CN2, comparison neighborhood 2. [A
tabular version of this figure is also
available.]

We analyzed the combined proportion of moderate and vigorous activity for
youth and adults separately and the patterns of moderate and vigorous activity
were similar (combined proportions shown) (Figure 2).

The playground was open for 81 weeks, from July 2007 through April 2009. The
daily counts of playground users ranged from 1 to 114 (mean daily count, 25
users; data not shown).

Discussion

We found that after a walking path was installed in a low-income
neighborhood, the proportion of people observed who were active in that area
increased. Observed activity decreased in the other areas during the same
period. Self-reported activity also increased for residents in both intervention
areas on streets, for everyone in parks, and for those in INA on a walking
trail. However, only the change in park use was significant. The modest
increases in observed activity around the path suggest that improvements to the
built environment may have had this effect and are encouraging.

It must be noted that the evaluation of the path was not targeted around the
path. We hypothesized that environmental changes available to the entire
community would lead to increases in PA throughout the neighborhood, as norms
for PA changed. However, we could not focus data collection where the built
environment was altered because the interventions were decided after baseline
data were gathered. Regardless, we were able to show a modest increase in both
observed moderate and vigorous activity combined from 36.7% to 41.0% and in
observed vigorous activity from 10.5% to 13.7%.

Randomized, controlled trials involving changes to the built environment are
difficult to conduct, so most research has been cross-sectional. Our study used
cross-sectional assessments before and after the interventions were implemented.
In a review article relating features of the built environment to PA in majority
African American populations, associations have not been consistent (16). We
were unable to find other studies that used objectively measured PA to evaluate
interventions to the built environment in primarily African American settings.
Objectively measured activity is a better measure of the changes occurring in
the neighborhood (17). We also saw some correspondence with the self-reported
findings on increase in walking trail use. Our finding is encouraging and
confirms cross-sectional studies that find a relationship between features in
the built environment and an increase in PA.

Trails and other outdoor recreational spaces in the physical environment
promote PA, as evidenced by this study and others (6,18,19). Community-based
interventions such as implementing walking paths and trails are a strategy to
increase PA levels of residents. A key component to a successful walking path or
trail is the ability of residents to have access near their homes and to provide
connectivity by linking to destinations (17,20-23). The path in this study was
centrally located in the neighborhood and connected a well-used park to a
commercial district with markets, shops, and businesses that the neighborhood
residents frequent.

Environmental interventions aimed at increasing PA have shown modest results.
Fitzhugh et al found an association between objectively measured PA and
increased PA after installation of a greenway/trail (24). Their study placed
observers in a stationary position in the neighborhood and only captured people
being active at the observed location. Our methods allowed us to observe the
activity of the entire neighborhood and to count sedentary as well as active
people.

The study has several limitations. First, respondents tend to overestimate
duration and intensity of PA when the accounts are self-recalled (25). However,
systematic observations such as the ones we used are reliable measures of the
use of a place or space (26) and may overcome the limitations of self-report
(27). Second, when neighborhood is the unit of analysis, randomization is nearly
impossible to accomplish, and matching for comparison is even more difficult
than matching individuals, making it challenging to control for confounding
variables. However, we chose 2 comparable neighborhoods, using census data and
the subjective experience and perceptions of steering committee members. The
data suggest that these efforts were successful and that communities were
well-matched on key variables. Third, the PA observations were limited to
streets and yards of houses. Parking lots, areas of commercial lots, and the
fenced area of the school yard were not captured in the observations. We were
not able to determine whether the users of the school playground were simply
displaced from the streets where they may have otherwise played or were new
users who were not previously playing outside. Fourth, evaluations of the
neighborhoods were broad and encompassed the entire area both in sampling
households throughout the neighborhood for the survey and in observing every
street in each neighborhood for PA. A more targeted evaluation of the area where
the interventions were implemented may have yielded a better measure of effect.

This study has several strengths. The study design included 2 comparison
communities, subjective and objective measures of the outcome variables, and 2
waves of data collection. The communities were chosen using objective
considerations as well as via the participation of members of the steering
committee, who had "insider" perspectives. The interventions were community
generated or supported, making them more likely to be accepted and used (11).
Contextual variables unique to the time and place (post-Katrina New Orleans)
were carefully considered and effectively measured.

Built environment changes, such as easily accessible paths that lead to
destinations, can provide more opportunities for PA in primarily African
American neighborhoods and others where infrastructure has been allowed to fall
into disrepair or was not initially installed. Such increases in opportunity can
lead to increases in population-level PA. Environmental interventions can affect many people, over a long period, which programmatic
interventions cannot do. Cities and planning commissions should prioritize
places for people to be active that are in or near the neighborhoods where
people live and consider simple adaptations to existing infrastructure. Resident
participation in decision making about the nature and location of environmental
changes may be necessary to ensure that changes lead to changes in
behavior. As PA levels decrease across the country and in specific communities,
creating supportive environments for activity is important. Walking paths or
trails may provide a long-term, sustainable, and low-cost strategy to increase
the PA of residents in low-income neighborhoods.

Acknowledgments

This study was part of the core research project of the Prevention Research
Center at Tulane University School of Public Health and Tropical Medicine and
was funded by Centers for Disease Control and Prevention Cooperative Agreement
no. 1-U48-DP-000047. The authors thank the PACE steering committee for their
dedicated time to the project.

Author Affiliations: Janet Rice, Kathryn M. Parker, Prevention Research
Center at Tulane University School of Public Health and Tropical Medicine, New
Orleans, Louisiana; Adam B. Becker, Consortium to Lower Obesity in Chicago
Children, Chicago, Illinois; Thomas A. Farley, New York City Department of
Health and Mental Hygiene, New York, New York.

Steps to a Healthier Louisiana (a project of the New Orleans City Health
Department and the Louisiana Public Health Institute)

The Renaissance Project (2004-2007)

Tulane University School of Public Health and Tropical Medicine

Urbanheart (2004-2005)

The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions.